Create app.py

app.py

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+import streamlit as st
+import tensorflow as tf
+import pickle
+import numpy as np
+from tensorflow.keras.preprocessing.sequence import pad_sequences
+import os
+import time
+from dotenv import load_dotenv
+
+load_dotenv()
+
+# Load the trained model
+model = tf.keras.models.load_model("best_binary_model_after_tuning.h5")
+
+# Load the tokenizer
+with open("binary_tokenizer.pkl", "rb") as handle:
+    tokenizer = pickle.load(handle)
+
+# Define fixed categories for 'type'
+type_options = ["Change", "Incident", "Problem", "Request"]
+
+# Define hardcoded label mapping for encoded results
+priority_mapping = {0: "Low", 1: "Med/High"}
+
+# Constants
+MAX_LENGTH = 512  
+
+# Function to preprocess text input
+def preprocess_text(text):
+    sequence = tokenizer.texts_to_sequences([text])
+    padded_sequence = pad_sequences(sequence, maxlen=MAX_LENGTH, padding='post', truncating='post')
+    return padded_sequence
+
+# Function to preprocess categorical input (type)
+def preprocess_type(selected_type):
+    mapping = {val: idx for idx, val in enumerate(type_options)}
+    return np.array([[mapping[selected_type]]])
+
+# Function to make predictions
+def generate_prediction(text_input, type_input):
+    features_combined = np.concatenate([text_input, type_input], axis=1)
+    prediction = model.predict(features_combined)[0][0]  # Get the probability
+    predicted_label = int(prediction > 0.5)  # Convert to 0 or 1
+    return priority_mapping[predicted_label]
+
+# Streamlit UI
+st.title("Resolve AI")
+st.write("Enter your request and select a type to generate a prediction.")
+
+user_input = st.text_area("Enter your text:", "")
+type_selection = st.selectbox("Select type:", type_options)
+
+if st.button("Generate Prediction"):
+    if user_input:
+        text_input = preprocess_text(user_input)
+        type_input = preprocess_type(type_selection)
+        predicted_priority = generate_prediction(text_input, type_input)
+        
+        st.write(f"Predicted priority: {predicted_priority}")
+        
+        if predicted_priority == "Med/High":
+            st.warning("This issue may require human intervention. Please contact support.")
+        else:
+           chatbot_link = 'https://huggingface.co/spaces/kdevoe/ResolveAI'
+           st.write('Please chat with our [assistant](%s) for further resolution'% chatbot_link)